class_docstr: Apply resize to image and bounding boxes.
  
  Parameters
  ----------
  width : int
      The target output width.
  height : int
      The target output height.
  
  Inputs:
      - **data**: input tensor with (Hi x Wi x C) shape.
      - **bbox**: input tensor with shape (N, 4+) where N is the number of bounding boxes.
          The second axis represents attributes of the bounding box.
          Specifically, these are :math:`(x_{min}, y_{min}, x_{max}, y_{max})`,
          we allow additional attributes other than coordinates, which stay intact
          during bounding box transformations.
  
  Outputs:
      - **out**: Cropped image with shape (H x W x C)
      - **bbox**: Cropped bounding boxes with shape :obj:`(M, 4+)` where M <= N.
          Tuple of length 4 as :math:`(x_{min}, y_{min}, x_{max}, y_{max})`.
code: def __init__(self, width, height, interp=1):
      super(ImageBboxResize, self).__init__()
      self._size = (width, height)
      self._interp = interp
  
imports: import random
  from .......base import numeric_types
  from ......block import Block
  from .......util import is_np_array
  from .......None import ndarray, numpy_extension, numpy
  from .utils import _check_bbox_shape, bbox_crop, bbox_translate
  from .utils import bbox_resize, bbox_random_crop_with_constraints
label_desc: Use image node_id 1 to create a new ImageBboxResize object from inherited base classes: Block with example: obj = ImageBboxResize(width, height, interp)